An estuary and bay warning management method and system based on digital twin

Abstract

The present invention discloses a method and system for estuary and bay early warning management based on digital twin, which relates to the technical field of early warning management, and includes the following steps: collecting multi-source data of the estuary and bay area and integrating them into a unified digital twin system; using the collected multi-source data to construct a digital twin model of the estuary and bay, and analyzing the dynamic trends of the physical water quality environment, hydrological environment and ecological environment of the estuary and bay; and deploying sensors and monitoring equipment to conduct real-time monitoring of the estuary and bay environment. The present invention scientifically identifies potential ecological problems in the ecosystem of the estuary and bay area, evaluates and predicts the performance of the ecosystem, optimizes the comprehensive management and preventive maintenance plans of land-sea coordination and river-sea linkage, improves the ecological functions of the bay, reduces decision-making risks, improves response effects and management efficiency, and explores new technical approaches and provides technical support for the sustainable high-quality development of the estuary and bay ecosystem.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning management, and particularly relates to a method and system for early warning management of estuaries and bays based on digital twins. Background Art

[0002] Currently, traditional methods for early warning management of estuaries and bays have obvious deficiencies in terms of real-time update and dynamic simulation capabilities. They often cannot quickly integrate and analyze new data, and it is difficult to analyze the carrying capacity of estuaries and bays for different pollutants under different estuary and bay states, resulting in a lack of response capabilities to environmental state changes. Therefore, when facing complex and changeable environmental problems, traditional methods often cannot meet the targeted needs of ecological environmental protection and management of estuaries and bays. For this reason, a method and system for early warning management of estuaries and bays based on digital twins are proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for early warning management of estuaries and bays based on digital twins to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, a method for early warning management of estuaries and bays based on digital twins includes the following steps:

[0006] Step 1: Collect multi-source data of the estuary and bay area and integrate it into a unified digital twin system, where the multi-source data includes water quality, meteorology, marine environment, and pollutant emissions;

[0007] Step 2: Use the collected multi-source data to construct a digital twin model of the estuary and bay, and analyze the dynamic trends of the physical water quality environment, hydrological environment, and ecological environment of the estuary and bay;

[0008] Step 3: Deploy sensors and monitoring devices to monitor the estuary and bay environment in real time, and update the monitoring data to the digital twin model in real time for dynamic simulation;

[0009] Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, and evaluate the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment;

[0010] Step 5: According to the risk status assessment results, combined with the environmental standards and protection objectives of the estuary and bay, formulate an early warning level classification standard, and clarify the early warning level of the estuary and bay environment in the evaluation area;

[0011] Step 6: According to the classification results of the early warning levels, automatically trigger the early warning system, send early warning information to the management personnel, and initiate corresponding early warning management measures and emergency response plans to implement intelligent early warning management.

[0012] A further improvement of the technical solution of the present invention lies in: in the said Step 1, the process of collecting multi-source data in the estuary and bay area is as follows:

[0013] Clarify the requirements for early warning management in the estuary and bay area, and determine the types of data to be collected, which are water quality, meteorology, hydrology, and pollution source emission data respectively;

[0014] Identify the data sources, and collect multi-source data of the determined types in the estuary and bay area. Among them, use on-line water quality monitoring technology to obtain water quality data through sensors and monitoring equipment, obtain meteorological data through meteorological observation stations and satellite remote sensing means, use ocean observation equipment, buoys, and underwater robots to obtain marine environment data, and obtain pollution source emission data through sewage monitoring equipment and industrial emission records;

[0015] Perform preprocessing operations such as cleaning, denoising, and format conversion on the collected multi-source data to ensure the quality and consistency of the data. Use data fusion technology to integrate and correlate data from different sources and in different formats to form a unified data view, and establish the spatio-temporal relationship between the data to achieve spatio-temporal matching and synchronization of the data;

[0016] According to the actual situation in the estuary and bay area, construct a unified digital twin system, and integrate and store the multi-source data into the digital twin system, and establish a data indexing and query mechanism to improve the efficiency of data retrieval and access.

[0017] A further improvement of the technical solution of the present invention lies in: in the said Step 2, the process of constructing the estuary and bay digital twin model is as follows:

[0018] Extract the preprocessed multi-source data, fuse them to form a complete data set, and construct a three-dimensional geometric model of the estuary and bay according to the geographical features and topographical information of the estuary and bay. Combine the spatial distribution characteristics of meteorology, hydrology, water quality, and pollution data to construct corresponding physical field models;

[0019] Use computer technology and numerical simulation methods to digitally represent the structure, performance, and operating state information of physical entities to form a visual virtual system, and construct a multi-process coupling model system, including meteorological models, hydrological models, water quality models, and pollution diffusion models, to simulate the dynamic changes of the meteorological environment, hydrological and water quality environment, and pollution interference in the estuary and bay.

[0020] Establish a real-time data connection to connect physical entities and digital models, achieve real-time synchronization and update of data, and build a data interaction platform to realize data exchange and interaction between digital models and physical entities;

[0021] Use a meteorological model to simulate the meteorological change process in estuaries and bays, analyze the spatio-temporal distribution characteristics of meteorological elements such as wind speed, wind direction, temperature, humidity, and dew point temperature, and combine with meteorological data to obtain a meteorological interference index to evaluate the impact of meteorological changes on the ecological environment and human activities in estuaries and bays;

[0022] Use a hydrological model to simulate the hydrological change process in estuaries and bays, analyze the spatio-temporal distribution characteristics of hydrological elements in estuaries and bays, use a water quality model to simulate the water quality change process in estuaries and bays, analyze the spatio-temporal distribution characteristics of water temperature, dissolved oxygen, suspended solid concentration, and salinity indicators, and combine with hydrological and water quality data to calculate a hydrological and water quality assessment index to evaluate the impact of hydrological and water quality changes on the ecological environment and human activities in estuaries and bays;

[0023] Use a pollution diffusion model to simulate the diffusion process of pollution sources in estuaries and bays, analyze the spatio-temporal distribution characteristics of pollutant concentration, suspended solid concentration, and nutrient concentration indicators, combine with pollution data to calculate a pollution interference assessment index to evaluate the pollution degree and influence range of pollution sources on the ecological environment in estuaries and bays.

[0024] A further improvement of the technical solution of the present invention lies in that: the calculation expression of the meteorological interference index is:

[0025] ;

[0026] wherein, MI is the meteorological interference index, T is the actual temperature, is the reference temperature, and the annual average temperature in the estuary and bay area is selected, is the historical highest temperature, is the historical lowest temperature, W is the actual wind speed, is the reference wind speed, and the annual average wind speed in the estuary and bay area is selected, is the historical highest wind speed, is the historical lowest wind speed, is the actual dew point temperature, is the reference dew point temperature, RH is the actual relative humidity, is the reference relative humidity. It should be noted that the value range of MI is from 0 to 1, where 0 indicates no meteorological interference and 1 indicates extreme meteorological interference;

[0027] The calculation expression of the hydrological and water quality assessment index is:

[0028] ;

[0029] Among them, SI is the hydrological water quality assessment index, WT is the actual water temperature, is the reference water temperature, and the annual average water temperature in the estuary and bay area is selected. is the change range of the water temperature, DO is the actual dissolved oxygen concentration, is the reference dissolved oxygen concentration, is the historical highest dissolved oxygen concentration, TS is the actual total suspended solid concentration, is the reference total suspended solid concentration, is the historical highest total suspended solid concentration, S is the actual salinity, is the reference salinity, and the annual average salinity in the estuary and bay area is selected. is the change range of the salinity. It should be noted that the value range of SI is between 0 and 1, where 0 represents extremely poor hydrological water quality conditions and 1 represents extremely good hydrological water quality conditions;

[0030] The calculation expression of the pollution interference assessment index is as follows:

[0031] ;

[0032] Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, is the reference pollutant concentration, and the annual average pollutant concentration in the estuary and bay area is selected. is the change range of the pollutant concentration, TS is the actual total suspended solid concentration, is the reference total suspended solid concentration, is the historical highest total suspended solid concentration, N is the actual nutrient concentration, is the reference nutrient concentration, is the change range of the nutrient concentration. It should be noted that the value range of PI is between 0 and 1, where 0 represents no pollution interference and 1 represents extreme pollution interference.

[0033] A further improvement of the technical solution of the present invention lies in that: in the third step, the process of dynamic simulation is as follows:

[0034] According to the requirements of estuary and bay environmental monitoring, sensors and monitoring equipment are deployed at relevant positions in the estuary and bay environment to ensure comprehensive coverage and monitoring of data on meteorology, hydrology, water quality, and pollution;

[0035] The sensors and monitoring equipment transmit the real-time monitored data to the data acquisition system, and preprocess the collected data, including data cleaning, format conversion, and data storage, to ensure the accuracy and availability of the data. The preprocessed data is updated to the digital twin model in real time to ensure the real-time and accuracy of the data, and dynamic simulation and analysis are carried out in the digital twin model;

[0036] Use a digital twin model for dynamic simulation to simulate the changes in the estuary and bay environment under different conditions, and update the state of the digital twin model according to real-time monitoring data to reflect the real-time changes in the estuary and bay environment;

[0037] Analyze the dynamic trends of the estuary and bay environment through the simulation results of the digital twin model, and evaluate the impacts of changes in meteorology, hydrology, water quality, and pollution on the ecological environment and human activities.

[0038] A further improvement of the technical solution of the present invention lies in that: in the fourth step, the analysis process of the comprehensive risk state of the estuary and bay environment is as follows:

[0039] According to the actual situation of the estuary and bay, set different pollution emission scenarios, including industrial emissions, agricultural emissions, and urban runoff, and determine the emission sources, emission intensities, and emission times of each pollution emission scenario;

[0040] Use the digital twin model to simulate the pollutant diffusion process of each emission source under different emission intensities and times, analyze the processes of pollutant transport, transformation, and degradation in the water body, and the impacts of pollutants on the ecological environment;

[0041] According to the simulation results, evaluate the environmental capacity of the estuary and bay under different pollution emission scenarios, where the environmental capacity refers to the maximum amount of pollutants that the estuary and bay can bear without damaging the health of the ecosystem;

[0042] Combine the meteorological interference index, hydrological and water quality assessment index, and pollution interference assessment index to comprehensively analyze and obtain the health risk assessment coefficient, and evaluate the health status of the estuary and bay ecosystem;

[0043] According to the evaluation results, analyze the overall health status and change trends of the estuary and bay ecosystem, and evaluate the comprehensive risk state of the estuary and bay environment.

[0044] A further improvement of the technical solution of the present invention lies in that: the calculation expression of the health risk assessment coefficient is:

[0045] ;

[0046] where HC is the health risk assessment coefficient, MI is the meteorological interference index, which reflects the degree of interference of meteorological conditions on the estuary and bay ecosystem, SI is the hydrological and water quality assessment index, is the reference value of the hydrological and water quality assessment index, which reflects the natural or undisturbed state of the hydrology and water quality of the estuary and bay, is the maximum value of the hydrological and water quality assessment index, reflecting the optimal value of the hydrological and water quality of the estuary and bay under ideal conditions. PI is the pollution interference assessment index, reflecting the degree of interference of pollution sources on the ecosystem of the estuary and bay. It should be noted that the value range of HC is between 0 and 1, where 0 indicates that the ecosystem of the estuary and bay is in a state of extremely high risk, and 1 indicates that the ecosystem of the estuary and bay is in the best health condition.

[0047] A further improvement of the technical solution of the present invention lies in that: in the step five, the determination process of the environmental warning level of the estuary and bay is as follows:

[0048] Analyze the environmental standards and protection objectives of the estuary and bay area, and combine the risk status assessment results and the severity of the risk level to divide different warning levels, namely the first-level warning level, the second-level warning level, the third-level warning level, and the fourth-level warning level. Among them, the severity of the risk level gradually increases from the first level to the fourth level;

[0049] Combine the health risk assessment coefficient to set corresponding risk assessment thresholds for each warning level. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered;

[0050] Through environmental monitoring equipment and sensor means, collect the environmental data of the estuary and bay in the evaluation area in real time, calculate the value of the health risk assessment coefficient and compare it with the divided risk assessment thresholds. Based on the comparison results, judge the warning level of the estuary and bay environment in the evaluation area;

[0051] Multiple said warning levels correspond to multiple said risk assessment thresholds, where the risk assessment thresholds include upper limit thresholds and lower limit thresholds;

[0052] Multiple said warning levels and multiple said risk assessment thresholds satisfy the following relationship:

[0053] First-level warning level ;

[0054] Second-level warning level ;

[0055] Third-level warning level ;

[0056] Fourth-level warning level ;

[0057] Among them, HC is the health risk assessment coefficient, is the lower limit threshold corresponding to the first-level warning level and the upper limit threshold corresponding to the second-level warning level, is the lower limit threshold corresponding to the second-level warning level and the upper limit threshold corresponding to the third-level warning level, It is the lower threshold value corresponding to the third-level warning level and the upper threshold value corresponding to the fourth-level warning level.

[0058] A further improvement of the technical solution of the present invention lies in that: in the step six, the process of sending warning information and implementing intelligent warning management is as follows:

[0059] According to the determined warning level, automatically trigger the warning system and prepare to send warning information, where the warning information includes the warning level, the affected area, and recommended countermeasures;

[0060] Send the warning information through multiple methods such as text messages, emails, phone calls, and mobile applications, so that the warning information is sent to the management personnel, environmental protection departments, emergency response teams, and relevant stakeholders in the estuary and bay areas;

[0061] According to the warning level, initiate corresponding warning management measures, strengthen the environmental monitoring frequency, restrict pollutant emissions, and issue environmental health tips. For the third-level and fourth-level warning levels, immediately initiate the emergency plan, which includes the emergency response process, emergency resource allocation, personnel evacuation plan, and ecological restoration measures.

[0062] In the second aspect, an estuary and bay warning management system based on digital twin is used to implement the estuary and bay warning management method based on digital twin, including a warning management center, which is communicatively connected to a data collection module, a digital twin model construction module, a data synchronization and update module, a risk warning module, a warning level classification module, and an intelligent warning management module. Among them, the modules are electrically connected to each other;

[0063] The data collection module is used to collect and integrate data from different sources, including meteorological, hydrological, water quality, and pollution data;

[0064] The digital twin model construction module combines the hydrological model and the meteorological model to construct a digital twin model of the estuary and bay;

[0065] The data synchronization and update module is used to update the collected data to the digital twin model in real time, analyze the latest state of the estuary and bay environment, and ensure the timeliness and accuracy of the data;

[0066] The risk warning module is used to evaluate the health status of the ecosystem and analyze the comprehensive risk status of the estuary and bay environment;

[0067] The warning level classification module divides different warning levels according to the risk status assessment results and environmental standards, and sets corresponding risk assessment thresholds. When the risk assessment indicators exceed the thresholds of the corresponding levels, trigger the warnings of the corresponding levels;

[0068] The intelligent early warning management module is used to automatically send early warning information to management personnel and initiate corresponding early warning management measures and emergency response plans.

[0069] Due to the adoption of the above technical solutions, the technical progress achieved by the present invention compared with the prior art is as follows:

[0070] The present invention provides a digital twin-based early warning management method and system for estuaries and bays. By scientifically identifying potential ecological problems in the ecosystem of estuary and bay areas, evaluating and predicting the performance of the ecosystem, optimizing the comprehensive management and preventive maintenance plans for land-sea coordination and river-sea linkage, improving the ecological functions of bays, reducing decision-making risks, enhancing response effects and management efficiency, it explores new technical approaches and provides technical support for the sustainable high-quality development of estuary and bay ecosystems.

[0071] The present invention provides a digital twin-based early warning management method and system for estuaries and bays. By evaluating the environmental capacity of estuaries and bays under different pollution emission scenarios, simulating the processes of pollutant transport, transformation and degradation, analyzing the impact of pollutants on the ecological environment, calculating the health risk assessment coefficient, and then determining the threshold of pollutant emissions, it provides a scientific basis for environmental management and policy formulation. According to the risk assessment results, a classification standard for early warning levels is formulated to clarify the early warning levels of the estuary and bay environment in the assessment area, thereby realizing the optimized management of environmental risks.

[0072] The present invention provides a digital twin-based early warning management method and system for estuaries and bays. By integrating a variety of sensors and monitoring devices, it realizes the all-round and real-time monitoring of the estuary and bay environment, can quickly collect and process meteorological, hydrological, water quality and pollution data, provides accurate environmental condition information, uses the digital twin model to simulate the diffusion and transformation processes of pollutants, improves the response speed to environmental changes, enables early warning information to be sent to management personnel in a timely manner, and thus takes prompt and effective response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0074] Figure 1 It is the flowchart of the method of the present invention;

[0075] Figure 2 It is the construction flowchart of the digital twin model of the estuary and bay of the present invention;

[0076] Figure 3It is the analysis flow chart of the comprehensive risk status of the estuary and bay environment of the present invention;

[0077] Figure 4 It is the module composition diagram of the present invention. Specific implementation manners

[0078] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0079] Embodiment 1, as Figure 1 、 Figure 2 shown, the present invention provides a digital-twin-based early warning management method for estuaries and bays, including the following steps:

[0080] Step 1, collect multi-source data of the estuary and bay area and integrate them into a unified digital-twin system. Among them, the multi-source data includes water quality, meteorology, marine environment, and pollution source emissions. Clarify the requirements for early warning management in the estuary and bay area, and determine the data types to be collected, namely water quality, meteorology, hydrology, and pollution source emission data. Identify the data sources, and collect the multi-source data of the estuary and bay area of the determined types. Among them, use on-line water quality monitoring technology to obtain water quality data through sensors and monitoring devices, including indicators such as oxygen, pH value, dissolved oxygen, total nitrogen, total phosphorus, and chemical oxygen demand in water, and regularly maintain and calibrate the monitoring devices to ensure the accuracy and reliability of the monitoring data. Obtain meteorological data through meteorological observation stations and satellite remote sensing means, including temperature, humidity, air pressure, wind speed, wind direction, precipitation, etc. Use meteorological models to interpolate and predict the data to improve the spatio-temporal resolution and accuracy of the data. Use marine observation equipment, buoys, and underwater robots to obtain marine environment data, including seawater temperature, salinity, flow velocity, flow direction, sea level height, etc. Obtain pollution source emission data through sewage monitoring equipment and industrial emission records, including the emissions and emission concentrations of pollutants such as wastewater, waste gas, and solid waste. Conduct statistical analysis on the emission data to identify the main pollution sources and emission characteristics. Perform preprocessing operations such as cleaning, denoising, and format conversion on the collected multi-source data to ensure the quality and consistency of the data. Use data fusion technology to integrate and correlate data from different sources and different formats to form a unified data view, and establish spatio-temporal relationships between the data to achieve spatio-temporal matching and synchronization of the data. According to the actual situation of the estuary and bay area, construct a unified digital-twin system, and integrate and store the multi-source data into the digital-twin system, and establish a data index and query mechanism to improve the efficiency of data retrieval and access;

[0081] Step 2: Using the collected multi-source data, construct a digital twin model of the estuary and bay, analyze the dynamic trends of the physical water quality environment, hydrological environment, and ecological environment of the estuary and bay, extract the preprocessed multi-source data, fuse them to form a complete data set, and construct a three-dimensional geometric model of the estuary and bay according to the geographical characteristics and topographical information of the estuary and bay. Combining the spatial distribution characteristics of meteorological, hydrological, water quality, and pollution data, construct the corresponding physical field models. Using computer technology and numerical simulation methods, digitally represent the structure, performance, and operating state information of physical entities to form a visual virtual system. Construct a multi-process coupling model system, including meteorological models, hydrological models, water quality models, and pollution diffusion models, to simulate the dynamic changes of the meteorological environment, hydrological and water quality environment, and pollution interference in the estuary and bay. Establish a real-time data connection to connect physical entities and digital models, achieve real-time synchronization and update of data, and construct a data interaction platform to realize data exchange and interaction between digital models and physical entities. Use the meteorological model to simulate the meteorological change process in the estuary and bay, analyze the spatio-temporal distribution characteristics of meteorological elements such as wind speed, wind direction, temperature, humidity, and dew point temperature, and combine with meteorological data to obtain the meteorological interference index to evaluate the impact of meteorological changes on the ecological environment and human activities in the estuary and bay. Use the hydrological model to simulate the hydrological change process in the estuary and bay, analyze the spatio-temporal distribution characteristics of hydrological elements in the estuary and bay. Use the water quality model to simulate the water quality change process in the estuary and bay, analyze the spatio-temporal distribution characteristics of water temperature, dissolved oxygen, suspended solid concentration, and salinity indicators, and combine with hydrological and water quality data to calculate the hydrological and water quality assessment index to evaluate the impact of hydrological and water quality changes on the ecological environment and human activities in the estuary and bay. Use the pollution diffusion model to simulate the diffusion process of pollution sources in the estuary and bay, analyze the spatio-temporal distribution characteristics of pollutant concentration, suspended solid concentration, and nutrient salt concentration indicators, combine with pollution data, calculate the pollution interference assessment index, and evaluate the pollution degree and influence range of pollution sources on the ecological environment in the estuary and bay;

[0082] Furthermore, the calculation expression of the meteorological interference index is:

[0083] ;

[0084] where MI is the meteorological interference index, T is the actual temperature, is the reference temperature, and the annual average temperature in the estuary and bay area is selected, is the historical highest temperature, is the historical lowest temperature, W is the actual wind speed, is the reference wind speed, and the perennial average wind speed in the estuary and bay area is selected, is the historical highest wind speed, is the historical lowest wind speed, is the actual dew point temperature, The reference dew point temperature, RH is the actual relative humidity, The reference relative humidity. It should be noted that the value range of MI is from 0 to 1, where 0 indicates no meteorological interference and 1 indicates extreme meteorological interference. When meteorological elements (such as temperature, wind speed, dew point temperature, and relative humidity) deviate from the reference value, the MI value will increase, indicating an increase in meteorological interference. When meteorological elements approach historical extreme values, the MI value will increase significantly, indicating a high degree of meteorological interference;

[0085] The calculation expression of the hydrographic and water quality assessment index is:

[0086] ;

[0087] Among them, SI is the hydrographic and water quality assessment index, WT is the actual water temperature, The reference water temperature, and the annual average water temperature in the estuary and bay area is selected. The variation range of water temperature, DO is the actual dissolved oxygen concentration, The reference dissolved oxygen concentration, The historical highest dissolved oxygen concentration, TS is the actual total suspended solid concentration, The reference total suspended solid concentration, The historical highest total suspended solid concentration, S is the actual salinity, The reference salinity, and the annual average salinity in the estuary and bay area is selected. The variation range of salinity. It should be noted that the value range of SI is between 0 and 1, where 0 indicates extremely poor hydrographic and water quality conditions and 1 indicates extremely good hydrographic and water quality conditions. When actual hydrographic and water quality parameters (such as water temperature, dissolved oxygen, total suspended solids, and salinity) deviate from the reference value, the SI value will decrease, indicating the deterioration of hydrographic and water quality conditions. When actual parameters approach historical extreme values, the SI value will decrease significantly, indicating serious deterioration of hydrographic and water quality conditions;

[0088] The calculation expression of the pollution interference assessment index is:

[0089] ;

[0090] Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, The reference pollutant concentration, and the annual average pollutant concentration in the estuary and bay area is selected. The variation range of pollutant concentration, TS is the actual total suspended solid concentration, The reference total suspended solid concentration, The historical highest total suspended solid concentration, N is the actual nutrient concentration, The reference nutrient concentration, is the variation range of nutrient concentration. It should be noted that the value range of PI is between 0 and 1, where 0 indicates no pollution interference and 1 indicates extreme pollution interference. When the actual pollution parameters (such as pollutant concentration, total suspended solids, and nutrient concentration) deviate from the reference value, the PI value will increase, indicating an increase in pollution interference. When the actual parameters approach the historical extreme values, the PI value will increase significantly, indicating a high degree of pollution interference;

[0091] Step 3: Deploy sensors and monitoring devices to conduct real-time monitoring of the estuary and bay environment, and update the monitoring data to the digital twin model in real time for dynamic simulation. According to the requirements of estuary and bay environment monitoring, deploy sensors and monitoring devices at relevant positions in the estuary and bay environment to ensure comprehensive coverage and acquisition of data on meteorology, hydrology, water quality, and pollution. The sensors and monitoring devices transmit the real-time monitored data to the data acquisition system, and preprocess the collected data, including data cleaning, format conversion, and data storage, to ensure the accuracy and availability of the data. Update the preprocessed data to the digital twin model in real time to ensure the timeliness and accuracy of the data. Conduct dynamic simulation and analysis in the digital twin model. Use the digital twin model for dynamic simulation to simulate the changes in the estuary and bay environment under different conditions, and update the state of the digital twin model according to the real-time monitoring data to reflect the real-time changes in the estuary and bay environment. Analyze the dynamic trends of the estuary and bay environment through the simulation results of the digital twin model, and evaluate the impacts of changes in meteorology, hydrology, water quality, and pollution on the ecological environment and human activities;

[0092] Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, and evaluate the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment;

[0093] Step 5: According to the results of the risk status assessment, combined with the environmental standards and protection objectives of the estuary and bay, formulate a warning level classification standard to clarify the warning level of the estuary and bay environment in the assessment area;

[0094] Step 6: According to the warning level classification results, automatically trigger the warning system, send warning information to the management personnel, and initiate corresponding warning management measures and emergency plans to implement intelligent warning management.

[0095] Example 2, as Figure 3 shown, based on Example 1, the present invention provides a technical solution: Preferably, in Step 4, the analysis process of the comprehensive risk status of the estuary and bay environment is as follows:

[0096] According to the actual situation of estuaries and bays, different pollution emission scenarios are set, including industrial emissions, agricultural emissions, and urban runoff. The emission sources, emission intensities, and emission times of each pollution emission scenario are determined. Among them, industrial emissions: the emission sources are industrial facilities such as chemical plants, oil refineries, and shipyards. The emission intensity is determined according to the production scale, process level, and wastewater treatment efficiency of the factory, usually relatively high. The emission time is mostly continuous emissions, but it is affected by the production cycle of the factory and the operation status of the wastewater treatment facilities. Agricultural emissions: the emission sources are agricultural lands such as farmlands, orchards, and farms. The emission intensity is affected by the type of agricultural activities, the usage amount of pesticides and fertilizers, and the intensity of rainwater scouring. It is usually seasonal. The emission time is mostly concentrated in the rainy season or the peak period of agricultural activities, such as sowing, fertilizing, and harvesting stages. Urban runoff: the emission sources are urban roads, rainwater pipes, residential areas, etc. The emission intensity is affected by rainfall, urban topography, drainage facilities, and human activity intensity. The emission time is mostly for a short time after heavy rain, but urban sewage emissions are continuous. Using the digital twin model, simulate the pollutant diffusion process of each emission source under different emission intensities and times, analyze the transmission, transformation, and degradation processes of pollutants in the water body and the impact of pollutants on the ecological environment. According to the simulation results, evaluate the environmental capacity of the estuary and bay under different pollution emission scenarios. Among them, the environmental capacity refers to the maximum amount of pollutants that the estuary and bay can bear without damaging the health of the ecosystem. Combining the meteorological interference index, the hydrological and water quality assessment index, and the pollution interference assessment index, comprehensively analyze to obtain the health risk assessment coefficient, and evaluate the health status of the estuary and bay ecosystem. According to the evaluation results, analyze the overall health status and change trend of the estuary and bay ecosystem, and evaluate the comprehensive risk status of the estuary and bay environment;

[0097] Furthermore, the calculation expression of the health risk assessment coefficient is:

[0098] ;

[0099] where HC is the health risk assessment coefficient, MI is the meteorological interference index, which reflects the interference degree of meteorological conditions on the estuary and bay ecosystem, SI is the hydrological and water quality assessment index, is the benchmark value of the hydrological and water quality assessment index, which reflects the natural or undisturbed state of the hydrological and water quality of the estuary and bay, is the maximum value of the hydrological and water quality assessment index, reflecting the optimal value of the hydrological and water quality in the estuary and bay under ideal conditions. PI is the pollution interference assessment index, reflecting the degree of interference of pollution sources on the ecosystem of the estuary and bay. It should be noted that the value range of HC is between 0 and 1, where 0 indicates that the ecosystem of the estuary and bay is in an extremely high-risk state, and 1 indicates that the ecosystem of the estuary and bay is in the best health condition. When the values of MI, SI, and PI increase, it means that the degree of interference of meteorology, hydrological and water quality, and pollution increases. At this time, the value of HC will decrease, reflecting the deterioration of the health condition of the ecosystem. When the value of HC approaches 1, it indicates that the health condition of the ecosystem is good and the various interference factors are small;

[0100] In step five, the process of determining the environmental warning level of the estuary and bay is as follows:

[0101] Analyze the environmental standards and protection objectives of the estuary and bay area, and combine the risk status assessment results and the severity of the risk level to divide different warning levels, namely the first-level warning level, the second-level warning level, the third-level warning level, and the fourth-level warning level. Among them, the severity of the risk level gradually increases from the first level to the fourth level. Combine the health risk assessment coefficient to set the corresponding risk assessment threshold for each warning level. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered. Through environmental monitoring equipment and sensor means, collect the environmental data of the estuary and bay in the evaluation area in real time, calculate the value of the health risk assessment coefficient and compare it with the divided risk assessment threshold. Based on the comparison result, judge the warning level of the estuary and bay environment in the evaluation area. Multiple warning levels correspond to multiple risk assessment thresholds. Among them, the risk assessment threshold includes the upper threshold and the lower threshold;

[0102] The multiple warning levels and multiple risk assessment thresholds satisfy the following relationship:

[0103] First-level warning level ;

[0104] Second-level warning level ;

[0105] Third-level warning level ;

[0106] Fourth-level warning level ;

[0107] Among them, HC is the health risk assessment coefficient, is the lower threshold corresponding to the first-level warning level and the upper threshold corresponding to the second-level warning level, is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the third-level warning level, is the lower threshold corresponding to the third-level warning level and the upper threshold corresponding to the fourth-level warning level;

[0108] In Step 6, the process of sending early warning information and implementing intelligent early warning management is as follows:

[0109] According to the determined early warning level, the early warning system is automatically triggered to prepare to send early warning information. The early warning information includes the early warning level, the affected area, and recommended countermeasures, and is sent through multiple methods such as text messages, emails, phone calls, and mobile applications, so that the early warning information is sent to the management personnel, environmental protection departments, emergency response teams, and relevant stakeholders in the estuary and bay areas. According to the early warning level, corresponding early warning management measures are initiated, including strengthening the frequency of environmental monitoring, restricting pollutant emissions, and issuing environmental health tips. For level-three and level-four early warning levels, the emergency response plan is immediately activated, which includes the emergency response process, emergency resource allocation, personnel evacuation plan, and ecological restoration measures.

[0110] Example 3, as Figure 4 shown, on the basis of Examples 1-2, the present invention also provides a digital-twin-based estuary and bay early warning management system for implementing the digital-twin-based estuary and bay early warning management method, including an early warning management center, which is communicatively connected to a data collection module, a digital twin model construction module, a data synchronization and update module, a risk early warning module, an early warning level classification module, and an intelligent early warning management module. Among them, the modules are electrically connected to each other;

[0111] The data collection module is used to collect and integrate data from different sources, including meteorological, hydrological, water quality, and pollution data;

[0112] The digital twin model construction module constructs a digital twin model of the estuary and bay by combining a hydrological model and a meteorological model;

[0113] The data synchronization and update module is used to update the collected data to the digital twin model in real time, analyze the latest state of the estuary and bay environment, and ensure the timeliness and accuracy of the data;

[0114] The risk early warning module is used to evaluate the health status of the ecosystem and analyze the comprehensive risk status of the estuary and bay environment;

[0115] The early warning level classification module divides different early warning levels according to the risk status evaluation results and environmental standards, and sets corresponding risk assessment thresholds. When the risk assessment indicators exceed the thresholds of the corresponding levels, the corresponding level of early warning is triggered;

[0116] The intelligent early warning management module is used to automatically send early warning information to the management personnel and initiate corresponding early warning management measures and emergency response plans.

[0117] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

Claims

1. An estuary and bay early warning management method based on digital twins, characterized by: The following steps are involved: Step 1: Collect multi-source data from the estuary and bay area and integrate them into a unified digital twin system; Step 2: Use the collected multi-source data to build a digital twin model of the estuary and bay, and analyze the dynamic trends of the physical water quality environment, hydrological environment, and ecological environment of the estuary and bay. In step 2, the construction process of the digital twin model of the estuary and bay is as follows: Extract the pre-processed multi-source data and fuse them to form a complete data set. According to the geographical characteristics and topographic information of the estuary, a three-dimensional geometric model of the estuary is constructed. Combined with the spatial distribution characteristics of meteorological, hydrological, water quality and pollution data, the corresponding physical field model is constructed. By using computer technology and numerical simulation methods, the structure, performance and operation status information of physical entities are digitally represented to form a visualized virtual system, and a multi-process coupling model system is constructed, including meteorological model, hydrological model, water quality model and pollution diffusion model, so as to simulate the dynamic changes of meteorological environment, hydrological and water quality environment and pollution interference in estuaries and bays; Establish real-time data connection, connect physical entities and digital models, realize real-time synchronization and update of data, and build a data interaction platform to realize data exchange and interaction between digital models and physical entities; Use meteorological models to simulate the meteorological changes in estuaries and bays, analyze the temporal and spatial distribution characteristics of meteorological elements such as wind speed, wind direction, temperature, humidity, and dew point temperature, and combine meteorological data to obtain the meteorological interference index to evaluate the impact of meteorological changes on the ecological environment and human activities in estuaries and bays; Use hydrological models to simulate the hydrological changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of hydrological elements in estuaries and bays, use water quality models to simulate the water quality changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of water temperature, dissolved oxygen, suspended solids concentration, and salinity indicators, and combine hydrological and water quality data to calculate the hydrological and water quality assessment index to assess the impact of hydrological and water quality changes on the ecological environment and human activities in estuaries and bays; Use the pollution diffusion model to simulate the diffusion process of pollution sources in estuaries and bays, analyze the temporal and spatial distribution characteristics of pollutant concentrations, suspended solid concentrations, and nutrient concentrations, and calculate the pollution interference assessment index based on pollution data to assess the pollution degree and impact range of pollution sources on the ecological environment of estuaries and bays; The calculation expression of the meteorological interference index is: Among them, MI is the meteorological interference index, T is the actual temperature, T0 is the reference temperature, T max The highest temperature in history, T min is the lowest temperature in history, W is the actual wind speed, W0 is the reference wind speed, and W max The highest wind speed in history, W min The lowest wind speed in history, H dew is the actual dew point temperature, H0 is the reference dew point temperature, RH is the actual relative humidity, RH0 is the reference relative humidity; The calculation expression of the hydrological and water quality assessment index is: Among them, SI is the hydrological and water quality assessment index, WT is the actual water temperature, WT0 is the reference water temperature, WT s is the range of water temperature, DO is the actual dissolved oxygen concentration, DO0 is the reference dissolved oxygen concentration, and DO max is the highest dissolved oxygen concentration in history, TS is the actual total suspended solids concentration, TS0 is the benchmark total suspended solids concentration, and TS max is the highest total suspended solids concentration in history, S is the actual salinity, S0 is the reference salinity, and S s is the range of salinity; The calculation expression of the pollution interference assessment index is: Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, C0 is the benchmark pollutant concentration, and C s is the range of pollutant concentration, TS is the actual total suspended solids concentration, TS0 is the benchmark total suspended solids concentration, and TS max is the highest total suspended solids concentration in history, N is the actual nutrient concentration, N0 is the benchmark nutrient concentration, and N s is the range of nutrient concentration; Step three: by deploying sensors and monitoring equipment, the estuary and bay environment is monitored in real time, and the monitoring data is updated in real time to the digital twin model for dynamic simulation. In step three, the dynamic simulation process is as follows: Deploy sensors and monitoring equipment at relevant locations of estuary and bay environments according to the needs of estuary and bay environment monitoring; Sensors and monitoring equipment transmit the real-time monitored data to the data acquisition system, pre-process the collected data, and update the pre-processed data to the digital twin model in real time, and perform dynamic simulation and analysis in the digital twin model; Use the digital twin model to conduct dynamic simulation to simulate the changes in the estuary bay environment under different conditions, and update the status of the digital twin model based on real-time monitoring data to reflect the real-time changes in the estuary bay environment; Analyze the dynamic trends of estuary and bay environments through simulation results of digital twin models, and assess the impact of changes in meteorology, hydrology, water quality and pollution on the ecological environment and human activities; Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, assess the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment; Step 5: Based on the risk status assessment results, combined with the environmental standards and protection objectives of estuaries and bays, formulate warning level classification standards and clarify the warning level of the estuary and bay environment in the assessment area; Step six: According to the warning level classification results, the warning system is automatically triggered to send warning information to management personnel, and corresponding warning management measures and emergency plans are initiated to implement intelligent warning management.

2. According to the digital twin-based estuary and bay early warning management method of claim 1, it is characterized by: In step 1, the process of collecting multi-source data in the estuary bay area is as follows: Clarify the needs for early warning management in estuary and bay areas and determine the types of data to be collected, namely water quality, meteorology, hydrology, and pollution source emission data; Identify data sources and collect multi-source data for estuaries and bays of a certain type, including using online water quality monitoring technology to obtain water quality data through sensors and monitoring equipment, obtaining meteorological data through meteorological observation stations and satellite remote sensing, obtaining marine environmental data through marine observation equipment, buoys, and underwater robots, and obtaining pollution source emission data through sewage monitoring equipment and industrial emission records; Perform pre-processing operations such as cleaning, denoising, and format conversion on the collected multi-source data. Use data fusion technology to integrate and associate data from different sources and formats to form a unified data view, establish the spatiotemporal relationship between data, and achieve spatiotemporal matching and synchronization of data. According to the actual situation of the estuary and bay area, a unified digital twin system is constructed, and multi-source data are integrated and stored in the digital twin system.

3. The estuary and bay early warning management method based on digital twin according to claim 2 is characterized by: In step 4, the analysis process of the comprehensive risk status of the estuary and bay environment is as follows: According to the actual situation of the estuary and bay, different pollution emission scenarios are set, including industrial emissions, agricultural emissions, and urban runoff, and the emission sources, emission intensity, and emission time of each pollution emission scenario are determined; Use digital twin models to simulate the pollutant diffusion process of each emission source at different emission intensities and times, analyze the transmission, transformation and degradation process of pollutants in water bodies, and the impact of pollutants on the ecological environment; Based on the simulation results, the environmental capacity of estuaries and bays under different pollution emission scenarios is evaluated; Combining the meteorological interference index, hydrological and water quality assessment index and pollution interference assessment index, a comprehensive analysis is conducted to obtain the health risk assessment coefficient and assess the health status of the estuary and bay ecosystem. Based on the assessment results, the overall health status and changing trends of the estuarine and bay ecosystems are analyzed, and the comprehensive risk status of the estuarine and bay environment is evaluated.

4. The estuary and bay early warning management method based on digital twin according to claim 3 is characterized by: The calculation expression of the health risk assessment coefficient is: Among them, HC is the health risk assessment coefficient, MI is the meteorological interference index, SI is the hydrological and water quality assessment index, SI0 is the benchmark value of the hydrological and water quality assessment index, and SI max is the maximum value of the hydrological and water quality assessment index, PI is the pollution interference assessment index, and it should be noted that the value range of HC is between 0 and 1, where 0 means that the estuary and bay ecosystem is in an extremely high-risk state, and 1 means that the estuary and bay ecosystem is in the best health.

5. The estuary and bay early warning management method based on digital twin according to claim 4 is characterized by: In step 5, the process of determining the estuary and bay environmental warning level is as follows: Analyze the environmental standards and protection targets of the estuary and bay areas, and divide the warning levels into level 1, level 2, level 3 and level 4 based on the risk status assessment results and the severity of the risk level. The severity of the risk level increases gradually from level 1 to level 4. Combined with the health risk assessment coefficient, a corresponding risk assessment threshold is set for each warning level. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered; Through environmental monitoring equipment and sensors, the environmental data of the estuaries and bays in the assessment area are collected in real time, the value of the health risk assessment coefficient is calculated and compared with the risk assessment threshold, and based on the comparison results, the warning level of the estuary and bay environment in the assessment area is determined; A plurality of the warning levels correspond to a plurality of the risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple risk assessment thresholds satisfy the following relationship: Level 1 Warning HC y ≤HC<1; Level 2 warning level HC e ≤HC <HC y ; Level 3 warning level HC s ≤HC <HC e ; Level 4 warning level 0 <HC<HC s ; Among them, HC is the health risk assessment coefficient, HC y is the lower threshold corresponding to the first-level warning level and the upper threshold corresponding to the second-level warning level, HC e is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the third-level warning level, HC s It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the fourth warning level.

6. The estuary and bay early warning management method based on digital twin according to claim 5 is characterized by: In step 6, the process of sending warning information and implementing intelligent warning management is as follows: According to the determined warning level, the warning system is automatically triggered to prepare to issue warning information, which includes the warning level, impact range, and recommended response measures; Send warning information through SMS, email, phone, and mobile applications; According to the warning level, initiate corresponding warning management measures, increase the frequency of environmental monitoring, limit pollutant emissions, and issue environmental health alerts. For level three and level four warning levels, immediately activate the emergency plan, which includes emergency response procedures, emergency resource allocation, personnel evacuation plans, and ecological restoration measures.

7. An estuary and bay early warning management system based on digital twins, used to implement the estuary and bay early warning management method based on digital twins as described in any one of claims 1 to 6, comprising an early warning management center, characterized in that: The early warning management center is communicatively connected with a data collection module, a digital twin model construction module, a data synchronization update module, a risk early warning module, an early warning level classification module and an intelligent early warning management module, wherein the modules are connected by electrical signals; The data collection module is used to collect and integrate data from different sources, including meteorological, hydrological, water quality and pollution data; The digital twin model construction module combines the hydrological model and the meteorological model to construct a digital twin model of the estuary and bay; The data synchronization update module is used to update the collected data to the digital twin model in real time to analyze the latest status of the estuary and bay environment; The risk warning module is used to evaluate the health status of the ecosystem and analyze the comprehensive risk status of the estuary and bay environment; The warning level classification module divides different warning levels according to risk status assessment results and environmental standards, and sets corresponding risk assessment thresholds; The intelligent early warning management module is used to automatically send early warning information to management personnel and initiate corresponding early warning management measures and emergency plans.

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